Remove k-sampler requirement and remove it form the cold. not used anymore anyway

toolkit/sampler.py

@@ -20,8 +20,6 @@ from toolkit.samplers.mean_flow_scheduler import MeanFlowScheduler
 
 from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
 
-from k_diffusion.external import CompVisDenoiser
-
 from toolkit.samplers.custom_lcm_scheduler import CustomLCMScheduler
 
 # scheduler:
@@ -185,29 +183,3 @@ def get_sampler(
     scheduler = scheduler_cls.from_config(config)
 
     return scheduler
-
-
-# testing
-if __name__ == "__main__":
-    from diffusers import DiffusionPipeline
-
-    from diffusers import StableDiffusionKDiffusionPipeline
-    import torch
-    import os
-
-    inference_steps = 25
-
-    pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
-    pipe = pipe.to("cuda")
-
-    k_diffusion_model = CompVisDenoiser(model)
-
-    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
-    pipe = pipe.to("cuda")
-
-    prompt = "an astronaut riding a horse on mars"
-    pipe.set_scheduler("sample_heun")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
-
-    image.save("./astronaut_heun_k_diffusion.png")